Diabetes is one of the fastest growing diseases today. The World Health Organization estimates that 177 million people worldwide currently have diabetes and this number is projected to increase to more than 370 million people by the year 2030. The costs associated with diabetes, including premature death, pain and suffering, and increased financial burdens. These costs are directly related to the medical complications associated with chronic hyperglycemia. Early detection and maintaining a tight glycemic control are paramount to controlling the costs of the diabetic epidemic.
The cornerstone of tight glycemic control is frequent glucose monitoring, where blood glucose concentrations are measured to help administer proper levels of insulin and maintain euglycemic conditions. To this end, glucose sensing technology has advanced considerably in recent years to provide tools for home glucose monitoring and establishing opportunities for tight glycemic control. The current conventional determination of blood glucose is a routine invasive procedure typically performed several times a day. In general, this procedure involves the taking of a small blood sample and evaluating the level of glucose in the sample. Common instruments used for this use the enzyme glucose oxidase to convert glucose and oxygen to gluconic acid and hydrogen peroxide. The level of hydrogen peroxide is then measured by spectroscopic or electrochemical means which is reflective of the starting glucose concentration.
While these daily measurements provide a diabetic patient with the ability to self-monitor and thus better control blood glucose levels, they are not without drawbacks. In particular, the taking of blood samples several times daily can be very painful and expose the patient to elevated risks of infection. Moreover, these methods are not suitable for providing continuous blood glucose measurements. Thus, for example, during the night, a patient must either be awakened periodically for testing or else run the risk that glucose levels will drop dangerously low while they sleep.
Non-invasive optical sensing of an analyte, such as glucose, has been proposed as an approach for frequent and painless measurement of glucose in diabetics. However, to date, all reported attempts to measure glucose non-invasively have involved collecting spectra from a human and then using a classical statistical multivariate calibration technique to correlate variations in the spectral information to blood glucose concentrations. These statistical techniques rely on regressions to statistically correlate spectral variances to an artificially assigned glucose concentration. Thus, these measurements are not necessarily based on actual analyte specific spectral features. Further, these statistical methods fail to provide direct evidence that the assigned concentration predictions from the multivariate calibration models are actually based on glucose specific spectral information. Moreover, in some cases the in vivo spectral signature for a physiological analyte can be smaller than many weakly or partially correlated spectral variations, making the use of the conventional statistical methods very difficult.
Therefore, in view of the foregoing, there exists a need for an in vivo calibration method that can identify analyte specific spectral information. Further, there is also a need for a non-invasive method of measuring the concentration of an analyte in a test subject. Moreover, there is also a need for a method for evaluating the analytical significance of the classical statistical multivariate calibration models.